Interpretive Summary: Navigating AI deployment in precision livestock farming: current trends and future prospects

By: Chuanyi Guo, Zheng He, Mutian Niu, Kai Liu

Implications

• The selection of an AI deployment model is a critical strategic decision for livestock operations, as no single solution fits all scenarios.
• Cloud-edge collaborative architecture is emerging as the most effective paradigm, balancing on-farm responsiveness with powerful cloud analytics.
• Widespread AI adoption relies on overcoming key real-world barriers, including rural connectivity, implementation costs, and the on-farm technical skills gap.
• Future PLF advancements will depend on integrating multi-modal data to create more holistic and prescriptive animal health and welfare management systems.

Introduction

Precision livestock farming (PLF) is undergoing a profound transformation, with its core driver shifting from traditional data collection to intelligent decision-support systems powered by artificial intelligence (AI). While early-stage PLF relied on simple sensors for discrete tasks like estrus detection, rapid advancements in the Internet of Things (IoT), sensor technology, and computing power now enable modern systems to gather vast, multidimensional data covering animal behavior, physiology, and their micro-environment (Alexy & Haidegger, 2022; Kaur et al., 2023). This evolution is driven by multiple pressures facing the global livestock industry: rising labor costs and shortages compel farms to seek automation for efficiency, while increasing consumer and regulatory demands for product quality, animal welfare, and sustainability necessitate more refined management methods. Reflecting this momentum, the global PLF market is projected to expand at a compound annual growth rate of over 10% through the next decade, signaling strong and sustained industry adoption (Sojitra & Dudhagara, 2023). Consequently, AI’s role has evolved from a frontier concept to an indispensable engine for industry advancement.

The proliferation of data has catalyzed a surge in academic research focused on developing sophisticated AI algorithms to enhance livestock production, health, and welfare (He et al., 2025). These studies have demonstrated significant potential, with models capable of predicting metabolic diseases (Giannuzzi et al., 2023), detecting specific behaviors with superhuman accuracy (Kang et al., 2020), and optimizing feeding strategies (King et al., 2024). However, the majority of this research has concentrated on algorithmic innovation and validation in controlled environments. A critical gap persists between the development of high-performing algorithms and their practical, scalable, and robust implementation on commercial farms (Berckmans, 2017). The crucial questions of how these AI systems are deployed, the architectural trade-offs involved, and the real-world challenges encountered often remain underexplored. This disconnect hinders the translation of technological potential into tangible on-farm value.

Read more in Animal Frontiers: From Isolated Data to Integrated Ecosystems.